Fetching the paper…
Reading the bibliography…
Supervised deep learning has gained significant attention for speech enhancement recently.
“Performance measurement in blind audio source separation,”
E. Vincent, R. Gribonval, and C. Févotte, · 2006
Earlier work this paper cites.
Microphone array signal processing
Jacob Benesty, Jingdong Chen, and Yiteng Huang, · 2008
Earlier work this paper cites.
“Neural machine translation by jointly learning to align and translate,”
D. Bahdanau, K. Cho, and Y. Bengio, · 2014
Earlier work this paper cites.
“U-net: Convolutional networks for biomedical image segmentation,”
O. Ronneberger, P. Fischer, and T. Brox, · 2015
Earlier work this paper cites.
“The third ?chime? speech separation and recognition challenge: Dataset, task and baselines,”
J. Barker, R. Marxer, E. Vincent, and S. Watanabe, · 2015
Earlier work this paper cites.
“Improved mvdr beamforming using single-channel mask prediction networks,”
H. Erdogan, J. Hershey, S. Watanabe, M. Mandel, and J. Le Roux, · 2016
Earlier work this paper cites.
“Neural network based spectral mask estimation for acoustic beamforming,”
Jahn Heymann, Lukas Drude, and Reinhold Haeb-Umbach, · 2016
Earlier work this paper cites.
“A decomposable attention model for natural language inference,”
A. Parikh, O. Täckström, D. Das, and J. Uszkoreit, · 2016
Earlier work this paper cites.
“Complex ratio masking for monaural speech separation,”
D. S. Williamson, Y. Wang, and D. Wang, · 2016
Cited alongside, same era.
“Densely connected convolutional networks,”
G. Huang, Z. Liu, L. v. d. Maaten, and K. Q. Weinberger, · 2017
Cited alongside, same era.
“Multi-scale multi-band densenets for audio source separation,”
Naoya Takahashi and Yuki Mitsufuji, · 2017
Cited alongside, same era.
“Supervised speech separation based on deep learning: An overview,”
DeLiang Wang and Jitong Chen, · 2018
Cited alongside, same era.
“Combining spectral and spatial features for deep learning based blind speaker separation,”
Zhong-Qiu Wang and DeLiang Wang, · 2018
Cited alongside, same era.
“Multi-channel deep clustering: Discriminative spectral and spatial embeddings for speaker-independent speech separation,”
Zhong-Qiu Wang, Jonathan Le Roux, and John R Hershey, · 2018
“Wave-u-net: A multi-scale neural network for end-to-end audio source separation,”
D. Stoller, S. Ewert, and S. Dixon, · 2018
Later among the works it cites.
“End-to-end multi-channel speech separation,”
R. Gu, J. Wu, S. Zhang, L. Chen, Y. Xu, M. Yu, D. Su, Y. Zou, and D. Yu, · 2019
Later among the works it cites.
“Attention wave-u-net for speech enhancement,”
Ritwik Giri, Umut Isik, and Arvindh Krishnaswamy, · 2019
Later among the works it cites.
“Multichannel Speech Enhancement Based on Time-frequency Masking Using Subband Long Short-Term Memory,”
X. Li and R. Horaud, · 2019
Later among the works it cites.
“Phase-aware speech enhancement with deep complex u-net,”
H. Choi, J. Kim, J. Huh, A. Kim, J. Ha, and K. Lee, · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
“Time-frequency masking based online speech enhancement with multi-channel data using convolutional neural networks,”
S. Chakrabarty, D. Wang, and E. A. P. Habets, · 2018
Cited alongside, same era.
“Self-attention generative adversarial networks,”
H. Zhang, I. Goodfellow, D. Metaxas, and A. Odena, · 2018
Cited alongside, same era.
“Unsupervised speech enhancement based on multichannel nmf-informed beamforming for noise-robust automatic speech recognition,”
K. Shimada, Y. Bando, M. Mimura, K. Itoyama, K. Yoshii, and T. Kawahara, · 2019
Later among the works it cites.
“Simultaneous optimization of forgetting factor and time-frequency mask for block online multi-channel speech enhancement,”
M. Togami, · 2019
Later among the works it cites.